Bayesian rule-based complex background modeling and foreground detection

2010 ◽  
Vol 49 (2) ◽  
pp. 027006 ◽  
Author(s):  
Jong Geun Park
Keyword(s):  
Background Modeling ◽  
Foreground Detection ◽  
Rule Based ◽  
Complex Background

scholarly journals On the role and the importance of features for background modeling and foreground detection

2018 ◽  
Vol 28 ◽  
pp. 26-91 ◽  
Author(s):  
Thierry Bouwmans ◽  
Caroline Silva ◽  
Cristina Marghes ◽  
Mohammed Sami Zitouni ◽  
Harish Bhaskar ◽  
...  
Keyword(s):  
Background Modeling ◽  
Foreground Detection

scholarly journals Complex background model and foreground detection based on random aggregation

2015 ◽  
Vol 64 (15) ◽  
pp. 150701
Author(s):  
Bi Guo-Ling ◽  
Xu Zhi-Jun ◽  
Chen Tao ◽  
Wang Jian-Li ◽  
Zhang Yan-Kun
Keyword(s):  
Background Model ◽  
Foreground Detection ◽  
Complex Background ◽  
Random Aggregation

Human Motion Tracking and Alarm System Based on DaVinci

2014 ◽  
Vol 568-570 ◽  
pp. 647-651
Author(s):  
Yuan Yi Xiong ◽  
Jie Yang ◽  
Chuan Wang
Keyword(s):  
Motion Tracking ◽  
Warning System ◽  
Human Motion ◽  
Foreground Detection ◽  
Alarm System ◽  
Human Motion Tracking ◽  
Complex Background ◽  
Camshift Algorithm

The paper proposes a improved Camshift algorithm which solve the problem of the original Camshift that have limitations when the tracking target have similar color with the background and is obstructed. The paper combines codebook model with the Camshift. The YUV space is used in foreground detection rather than the RGB. The results of experiments show that the algorithm works well in complex background, occlusion and the same color interference. At last we achieve a warning system.

scholarly journals Local Compact Binary Count Based Nonparametric Background Modeling for Foreground Detection in Dynamic Scenes

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 92329-92340 ◽  
Author(s):  
Wei He ◽  
Yong K-Wan Kim ◽  
Hak-Lim Ko ◽  
Jianhui Wu ◽  
Wujing Li ◽  
...  
Keyword(s):  
Background Modeling ◽  
Foreground Detection ◽  
Dynamic Scenes ◽  
Compact Binary ◽  
Binary Count

Foreground Detection by Competitive Learning for Varying Input Distributions

2018 ◽  
Vol 28 (05) ◽  
pp. 1750056 ◽  
Author(s):  
Ezequiel López-Rubio ◽  
Miguel A. Molina-Cabello ◽  
Rafael Marcos Luque-Baena ◽  
Enrique Domínguez
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Input Data ◽  
State Of The Art ◽  
Background Modeling ◽  
Cluster Detection ◽  
Foreground Detection ◽  
Learning Mechanism ◽  
Input Distribution ◽  
Computer Vision Applications

One of the most important challenges in computer vision applications is the background modeling, especially when the background is dynamic and the input distribution might not be stationary, i.e. the distribution of the input data could change with time (e.g. changing illuminations, waving trees, water, etc.). In this work, an unsupervised learning neural network is proposed which is able to cope with progressive changes in the input distribution. It is based on a dual learning mechanism which manages the changes of the input distribution separately from the cluster detection. The proposal is adequate for scenes where the background varies slowly. The performance of the method is tested against several state-of-the-art foreground detectors both quantitatively and qualitatively, with favorable results.

scholarly journals Special issue on background modeling for foreground detection in real-world dynamic scenes

2013 ◽  
Vol 25 (5) ◽  
pp. 1101-1103 ◽  
Author(s):  
Thierry Bouwmans ◽  
Jordi Gonzàlez ◽  
Caifeng Shan ◽  
Massimo Piccardi ◽  
Larry Davis
Keyword(s):  
Real World ◽  
Background Modeling ◽  
Foreground Detection ◽  
Special Issue ◽  
Dynamic Scenes
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